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Pesticides and Veterinary Drugs Residues in Conventional Meat: A Food Safety Issue

2016· article· en· W2510444376 on OpenAlexvenueno aff
Irfan Khan, Saghir Ahmad

Bibliographic record

VenueJournal of Buffalo Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsnot available
FundersAligarh Muslim University
KeywordsVeterinary DrugsAgricultureBusinessFood safetyBiotechnologyVeterinary drugPesticidePesticide residueAgricultural scienceEnvironmental healthToxicologyMedicineFood scienceVeterinary medicineBiologyChemistry

Abstract

fetched live from OpenAlex

In the current scenario the most of people are well aware with health issues. Food safety is generally related with the quality of food i.e. whether the food product is standardised as according to national or international norms set by the statutory organisations. People can compromise with the nutritive values of food but not with their safety aspects. The meat and meat products carry the burden of harmful agents according to the production methods. Now-a-days the feedlot animals are being reared either through the natural farming (organic farming) or conventional farming method. Those methods produce safe and healthier meat because there is no use of harmful chemical agents’ viz., pesticides, herbicides, hormones, growth promoters, veterinary drugs and etc. On the other hand, in the conventional farming, all these chemical agents are used to enhance animal growth. Several chemical agents like pesticides and veterinary drugs residues may cause harmful health implications viz., teratogenicity, carcinogenicity, hypersensitivity reactions, gut bacterial resistance, toxicity and many more health problems in human beings. It is the thrust of today to replace the conventional meat with the organic meat to check the use of harmful chemical agents for a healthy social life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.251
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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